Abstract 857: Assuming tumors have a spherical shape in modeling EGFR mutant lung adenocarcinoma: Impact on modeled clinical outcome
Authors: Julie Kleine-Schultjann, Germán Gómez, Adèle L'Hostis, Jean-Louis Palgen, Claudio Monteiro, Perrine Masson
Journal: Cancer Research (2023)

Abstract Introduction The in silico epidermal growth factor receptor (EGFR) mutant lung adenocarcinoma (ISELA) model predicts tumor progression in patients with advanced EGFR-mutated lung adenocarcinoma [1]. To investigate model credibility, we dug into a simplifying assumption made in the ISELA model and in other models [2][3], namely the assumption that tumors have a spherical shape. The validity of the spherical assumption (SA) is assessed by analyzing two lung cancer datasets, and its impact on the model is assessed by comparing predictions of the ISELA model with different shape hypotheses. Material and Methods To evaluate the impact of tumor shape assumptions on the estimated tumor volume with respect to real-world data, two lung cancer datasets [4][5] were analyzed to appraise the sphericity of lung tumors. As individual longest tumor radii were available for each tumor, the estimated spherical volume was computed under the SA and compared to the reported tumor volume. The ellipsoid assumption (EA) was also explored as an alternative -less simplifying- shape assumption. As the three tumor axes are rarely reported and were unavailable in these datasets, the three tumor axes in the EA were defined with proportionality relationships to the longest available radius. The estimated elliptical volume was then compared to the measured volume. To quantify the impact of the SA on the model’s primary output, time to progression (TTP), an alternative ISELA model assuming ellipsoid tumors was implemented. Two clinical trial simulations -one under the SA and the other under the EA- were performed on the same virtual patients with only the sphericity parameter differing, thus allowing a patient per patient comparison. Results The datasets analysis revealed that under the SA the tumor volume was overestimated in most cases, whereas under the EA the tumor volume was better approximated. On average, tumors had their shortest axis equal to 0.7 times their longest radius. Comparison of predictions between simulations showed that only 5% of the virtual patients changed their treatment response status (non-responder/responder). In patients classified as responding to the treatment in both simulations, the median TTP difference was 14 days. Conclusion Both real data and in silico simulations enabled us to better understand to which extent the SA is a simplifying but yet credible assumption in modeling lung cancer progression. Considering ellipsoid tumors is nevertheless a promising alternative as it better predicts tumor volume. Additional studies are needed to further explore its use as support to clinical decision making. References [1] Jacob, et al. bioRxiv 2022. [2] Thomlinson, et al. British journal of cancer 1955. [3] Greenspan, Studies in Applied Mathematics 1972. [4] Dmitry, et al. The Cancer Imaging Archive 2017. [5] Jamal-Hanjani, et al. New England Journal of Medicine 2017. Citation Format: Julie Kleine-Schultjann, Germán Gómez, Adèle L'Hostis, Jean-Louis Palgen, Claudio Monteiro, Perrine Masson. Assuming tumors have a spherical shape in modeling EGFR mutant lung adenocarcinoma: Impact on modeled clinical outcome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 857.